US7890915B2  Statistical delay and noise calculation considering cell and interconnect variations  Google Patents
Statistical delay and noise calculation considering cell and interconnect variations Download PDFInfo
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 US7890915B2 US7890915B2 US11/918,760 US91876006A US7890915B2 US 7890915 B2 US7890915 B2 US 7890915B2 US 91876006 A US91876006 A US 91876006A US 7890915 B2 US7890915 B2 US 7890915B2
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 101700047065 STAC family Proteins 0 description 1
 VBIIFPGSPJYLRRUHFFFAOYSAM Stearyltrimethylammonium chloride Chemical compound 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 238000005315 distribution function Methods 0 description 1
 238000005516 engineering processes Methods 0 description 1
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Images
Classifications

 G—PHYSICS
 G06—COMPUTING; CALCULATING; COUNTING
 G06F—ELECTRIC DIGITAL DATA PROCESSING
 G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
 G06F17/50—Computeraided design
 G06F17/5009—Computeraided design using simulation
 G06F17/5022—Logic simulation, e.g. for logic circuit operation
 G06F17/5031—Timing analysis
Abstract
Description
This application claims priority from U.S. provisional application 60/663,219, filed Mar. 18, 2005, and the contents of which are incorporated in their entirety as if fully set forth herein.
Not applicable.
1. Field of the Invention
The invention generally relates to the field of integrated circuit design performance analysis and optimization, and particularly to the delay and crosstalk noise calculation for logic cells used in statistical static timing analysis of digital integrated circuit.
2. Description of the Related Art
In modern very largescale integrated circuit (VLSI) design, it is very important to improve the circuit operating speed and to verify if the circuit can perform at a target frequency. To achieve these goals, circuit designers extensively use timing verification and optimization software from Electronic Design Automation (EDA) vendors on their designs. Two main methodologies for timing verification are used: 1) transistorlevel simulation based method and 2) cell/gatelevel static timing analysis. The transistorlevel simulation method can accurately simulate the circuit timing behavior, but this method is very timeconsuming and is not feasible for a fullchip analysis. Static timing analysis provides a fast method to estimate circuit timing performance, and can be used for fullchip analysis.
In VLSI digital circuits, logic cells are the basic building blocks; logic cells are interconnected with metal wires. In static timing analysis, logic cell delay models are becoming more and more complicated as semiconductor technologies evolve. Prior to the 1980s, cell delays could be modeled as a constant number. During the 1980s, CMOS technologies were widely used, and cell delays became a function of input transition time and load capacitance. Early in the 1990's, due to interconnect scaling, logic cell delays became a function of gate and RC (resistance capacitance) interconnect loading. In the early 2000s, the increased thickness of metal wire (relative to the feature size) has resulted in strong coupling capacitance between different interconnects; and logic cell delay has become a function of coupling interconnect (i.e., crosstalk).
Moreover, the further decrease in feature sizes for nanoscale CMOS technologies increases the importance of process variations. These variations introduce uncertainty in circuit behaviors and significantly impact the circuit performance and product yield. The increased variability has given a new set of problems for circuit timing analysis. However, current delay calculation methods do not handle process and environmental variations from both cells and interconnects. The cornerbased methodology for worstcase analysis traditionally used in static timing analysis may be overly pessimistic as well as extremely inaccurate. A better circuit timing methodology is needed to more accurately account for circuit behavior as it is influenced by process variations.
Attempts to solve the statistical timing analysis problem can be largely be categorized as being in one of two approaches: either a pathbased approach or a blockbased approach. (See J. A. G. Jess and K. Kalafala et al, “Statistical timing for parametric yield prediction of digital integrated circuits”, Design Automation Conference (DAC), pp. 932937, June 2003; H. Chang and S. S. Sapatnekar, “Statistical Timing Analysis Considering Spatial Correlations using a Single Pertlike Transversal”, ICCAD 2003, pp. 621625, November 2003; Aseem Agarwal, David Blaauw, Vladimir Zolotov and Sarma B. K. Vrudhula, “Statistical Timing Analysis Using Bounds”, DATE 2003, pp. 1006210067; Anirudh Devgan and Chandramouli Kashyap, “Blockbased Static Timing Analysis with Uncertainty”, ICCAD 2003, November 2003; Jiayong Le, Xin Li and L. Pileggi, “STAC: statistical timing analysis with correlation,” IEEE Design Automation Conference, 2004).
However, both path based and block based approaches focus not on delay calculation but on high level timing propagation problems wherein the delay is assumed (based on a simple model) rather than calculated. With the decreasing of feature size in semiconductor technology, statistical cell delay can no longer be modeled as a simple value or function. The nonlinear input waveform, the metal interconnect resistiveness, and nonlinear receiver capacitance all have strong effects on cell delay. While some of these factors are accounted for in nominal delay calculation techniques, other factors have yet to be modeled. Process variations cause these factors to have statistical distributions. Consequently, all nominal delay calculation approaches (such as, for example, the effective capacitance method) are not currently able to capture statistical information accurately. A statistical delay calculation methodology is needed for greater accuracy in statistical timing analysis.
Crosstalk between nanoscale size features also complicates statistical timing analysis. At nanoscale feature sizes, the dominant portion of wiring capacitance is the interlayer neighboring wire capacitance. Consequently, the delay of a gate can be greatly impacted by the switching activity on neighboring wires (see R. Arunachalam, K. Rajagopal and L. Pileggi, TACO: Timing Analysis with Coupling” Proceedings of the Design Automation Conference, pp. 266269, June 2000). Accounting for this crosstalk effect, therefore, is a critical part of the statistical timing analysis process.
The “crosstalk effect” becomes significant when the coupling capacitance between adjacent interconnects increases. A coupled interconnect system includes a victim net and several aggressor nets. For a good discussion of coupled interconnect systems, see R. Arunachalam, K. Rajagopal and L. Pileggi, TACO: Timing Analysis with Coupling” Proceedings of the Design Automation Conference, pp. 266269, June 2000—which is incorporated by reference as if fully set forth herein. A net is a set of nodes resistively connected. A net has one driver node, one or more fanout nodes, and may have a number of intermediate nodes that are part of the interconnect. “Fanout” is the ability of a logic gate to drive further logic gates; fanout refers to or is quantified by referring to the number of gates before voltage falloff causes errors.
An “aggressor net” is a net that has significant coupling capacitance to the victim net so as to be able to influence the delay of the victim gate. A gate is a logic unit or cell. Each net has its ground capacitances, and there are coupling capacitances between different nets. When circuit feature size decreases, the space between interconnects is reduced and the ratio of coupling capacitance and substrate capacitance increases proportionally.
The effects of crosstalk (“crosstalk effect”) pose two major problems. In the case where the victim net is quiet (nonswitching), capacitive crosstalk can induce noise (glitches) and potentially cause functional failures. For example, if a glitch happens when the clock signal of a register is switching, the data in the register may be flipped accidentally. Alternatively, in a case where the victim net is active, crosstalk can change the delay of the victim if the aggressor is also switching. If the aggressor is switching in the opposite direction, crosstalk can lead to an increase in delay, which may cause “setup time violations.” If the aggressor is switching in the same direction as the victim, crosstalk may lead to the delay decreasing, and may cause “hold time violations.”
What is needed is a method of statistical timing that accounts for crosstalk as well as accounting for cell delay and noise.
The invention provides a sensitivitybased statistical delay calculation methodology. The inventive method provides accurate nominal delay together with the delay sensitivities with respect to different circuit elements (e.g., cells, interconnects, etc.) and variational parameters such as fabrication process and environmental variations. The invention provides a statistical delay calculation methodology, which can efficiently calculate nominal delay and its sensitivity over different parameters. In the inventive statistical effective capacitance approach, all the sensitivity computations are based on closedform formulas; consequently, the inventive method provides, rapidly and at low cost, high accuracy and high numerical stability.
The invention also provides a method for calculating waveformbased statistical noise and crosstalk delay. By means of “builtin” noise waveform alignment techniques, the method can accurately calculate statistical noise waveform and its impact on delay. The invention taught herein applies statistical Max and Sum operations to statistical noise waveform and noise envelope calculations.
The invention taught herein is broadly represented in
The inventive method of calculating delay comprises a first step of calculating a statistical driving circuit, and includes the substeps of calculating statistical compact interconnect load; calculating nominal effective capacitance through an equilibrium equation; and calculating statistical driving Thevenin/Norton circuit.
Once the Thevenin circuit is parameterized, the invention farther provides for determining statistical delay and transition by: calculating the statistical transfer function to fanout pins; calculating the statistical voltage waveforms at the fanout pins; and calculating the statistical delay and the transition from the waveforms.
The inventive method further provides the step of statistical noise calculation including the substeps of: a) calculating statistical noise waveform and envelope for a given input pin of a given aggressor cell; b) repeating Step a for all input pins of a given aggressor cell; c) calculating the statistical Max of the noise envelopes from all input pins of a given aggressor cell; d) repeating Step c for all aggressor cells; e) calculating the statistical Sum of the noise envelopes from all aggressors cells.
Also taught is a method of crosstalk delay calculation that includes the steps of: calculating statistical output waveform as the statistical Sum of the statistical fanout waveform of the victim cell (the value of the statistical fanout waveform from the victim cell is provided by the delay calculation of the method taught herein) and statistical noise waveform from the aggressor cells (the value of the statistical noise waveform from the aggressor cells is provided by the noise calculation of the method taught herein) and calculating crosstalk delay from statistical waveform using an equation
where td=time delay, v=voltage and n=noise.
As can easily be appreciated, the system and method may be implemented via software—computer readable media—or any configuration of components capable of delivering instructions to a central processing unit of any computing device. Moreover, an apparatus for performing the invention as well as a product resulting from the invention are within the scope of the teaching and claims.
The present invention will be understood and appreciated more fully from the following detailed description taken in conjunction with the drawings in which:
The invention taught herein is broadly represented in
In modern VLSI digital circuit, logical cell delay is a function of different physical sources. Cell delay is a function of nonlinear input waveform, nonlinear receiver load capacitances, resistive interconnect load, crosstalk effect and process and environmental variations.
With the increasing effects of interconnect resistance, gate output waveforms becomes increasingly nondigital and can no longer be modeled as saturated ramps. To solve this problem, delay calculations with the Ceff (Coupled gate effective capacitance) concept is widely used to take into the RC shielding effect of an interconnect (see J. Qian, S. Pullela, and L. T. Pileggi, “Modeling the effective capacitance for the RC Interconnect of CMOS gates,” IEEE Trans. On ComputerAided Design, vol. 13 no. 12, pp. 15261534, December, 1994).
Once the Thevenin model is parameterized, the interconnect 406 is then attached to the linear gate model (see
Equivalent Statistical Driver Model Calculation
The preferred embodiment of the invention as regards delay calculation includes the steps as set forth herein. Initially, a statistical Thevenin model is constructed from statistical Effective Capacitance evaluation. Then a nominal Ceff evaluation is performed to construct an equivalent Thevenin model. (A detailed nominal Ceff evaluation process can be found in Mustafa Celik, Lawrence Pileggi and Altan Odabasioglu, “IC Interconnect Analysis”, Kluwer Academic Publishers, 2002 (incorporated by reference as if fully set forth herein)).
At this point, to aid the reader, the derivation is omitted and the resulting equation shown—the final average current expression for both π load and Ceff load from our modified nominal Ceff calculation. An equivalent Thevenin model can be computed by iteratively matching these two equations. As is shown in equations (1) and (2), to avoid unstable numerical computation, the inventive method improves the nominal Ceff evaluation by expressing the average current in turns of time constant.
Once the nominal Thevenin model is available, one can start to compute its sensitivity with respect to different circuit element and process parameters. To simplify the computation, we rewrite the Ceff expression in a general form as shown in equation (3) where S(si,w,Ceff) and T(si,w,Ceff) are output slew and time constant that are directly queried from cell table during Ceff iteration. Among all the variables in equation (3), only input slew (s_{i}) and π load (π=c_{1}, c_{2}, r_{pi}) are original variables. Considering process variation, we also introduce a new variable—w—which represents variational cell process parameters.
Ceff(si,π,w)=F(π,Ceff,S(si,w,Ceff),T(si,w,Ceff)) (3)
Note that this equation (3) should always be satisfied. Thus its derivatives with respect to every original variable should be equal to zero. Using the satisfied equation, we can calculate the sensitivities of Ceff to input slew, π load and cell process variables: dCeff/dsi, dCeff/dw and dCeff/dπ.
Once statistical Ceff is available, the sensitivity of Thevenin model with respect to different variables can be easily computed by applying the chain rule.
Statistical Waveform Propagation and Statistical Delay and Transition Calculation
Once the statistical Thevenin model is available, an interconnect can be attached, and fanout delay and transition can then be calculated through statistical fanout moment. As in the previous steps (see
From equation (4), we can calculate the sensitivity of the output waveform with respect to poles and zeros; the chain rule is then used to calculate the sensitivity for the Thevenin circuit and the interconnect load. Once waveform sensitivity has been thus determined, using the condition that v(t_{d})≡v_{d }and v_{d }is a fixed value, we can compute the timing sensitivity for a different fixed voltage point:
Statistical Noise and Crosstalk Delay Calculation
The invention provides a novel method for statistical noise analysis and crosstalk delay calculation (see
When the aggressor net is switching, its driving cell can be modeled as a Thevenin linear circuit. And the transfer function from aggressor driver pin to victim fanout pin can be calculated using momentbased methods. If we assume the transition time of the aggressor voltage source is Δ, and the transfer function is modeled as a twopole function where p1, p2, k1, k2 are corresponding poles and residues, the fanout noise waveform can be calculated using the following equations:
Noise waveform calculated in equation (6) has several useful features. First, noise waveform starts from zero (v(0)=0) and end up with zero (v(∞)=0). Second, there is one and only one peak on the waveform, and the voltage beforepeak and afterpeak decreases monotonically. Once the nominal waveform is calculated, it is straightforward to calculate the statistical waveform dv(t)/dw by applying the chain rule on equation (6).
Equation (6) gives the statistical noise waveform at victim net when aggressor net switches at time t=0. However, in static timing analysis, the exact switching time at a timing node is not known: only the earliest/latest arrival time is available. The aggressor cell/net input pin can switch at any time in this period between the earliest and the latest time available. In order to create the “worst case” noise scenario, we can combine the noise waveform with the arrival timing window to create a noise envelope.
A single noise envelope (see
There are two basic operations for noise envelope combination: Max and Sum. As shown in
Referring to

 a) Calculating statistical noise waveform and envelope for a given input pin of a given aggressor cell 905;
 b) Repeating Step a for all input pins of the aggressor cell 910;
 c) Calculating the statistical Max of the envelopes from all input pins of a given aggressor cell 915;
 d) Repeating Step c for all aggressors cells 920;
 e) Calculating the statistical Sum of noise envelopes from all aggressor cells 925.
Under process and environmental variations, noise waveforms and noise envelopes become statistical. To calculate the statistical noise waveform, the same “Max” and “Sum” concepts can be used. However, with process variations, these Max and Sum operations have to work on random variables instead of deterministic values.
Assume normal random variables x and y can be expressed as linear function of a set of independent random variables (p_{1}, p_{2 }. . . p_{n}), e.g. x=x_{0}+x_{1}·p_{1}+x_{2}·p_{2}+ . . . x_{n}·p_{n }and y=y_{0}+y_{1}p_{1}+y_{2}·p_{2}+ . . . x_{n}·p_{n}. The Sum operation is straightforward, s=(x+y)=(x_{0}+y_{0})+(x_{1}+y_{1})p_{1}+ . . . +(x_{n}+y_{1})p_{n}. For Max operation z=Max (x, y), analytical formulas exist so that variable z can also be approximated by the same set of random variables z=z_{0}+z_{1}·p_{1}+z_{2}·p_{2}+ . . . z_{n}·p_{n}. The parameters z_{0 }and z_{i }can be calculated from equation (7), where φ(•) and Φ(•) are the probability density function (PDF) and the cumulative distribution function (CDF) of normal random variables, a=sqrt(σ_{x} ^{2}+σ_{y} ^{2}−2σ_{x}σ_{y}ρ_{xy}) and α=(μ_{x}−μ_{y})/a.
The invention teaches using the statistical Sum and Max operations so as to calculate a final statistical noise waveform from individual noise waveforms. The invention teaches applying statistical Max and Sum operations to statistical noise waveform and envelope calculations. Referring again to
Calculating the statistical output waveform as the statistical Sum of the statistical fanout waveform from victim cell (from the statistical delay calculation) and statistical noise waveform from all aggressor cells 935; and
Calculating crosstalk delay from the statistical waveform using the equation 940
Crosstalk delay is a function of both noise waveform and original fanout waveform. Once available, total statistical noise waveform and statistical fanout waveform can be combined to calculate statistical crosstalk delay as a linear function of different process variables.
Theorem 1 can be proved by combining the statistical transition waveform and noise waveform and applying the chain rule. Because the transition waveform and the noise waveform both include statistical information, using Equation (9) we can easily calculate statistical crosstalk delay distributions.
As can easily be appreciated by those of skill in the relevant art, the system and method may be implemented via software—computer readable media—or in any configuration enabling the delivery of instructions for practicing the invention to any central processing unit of any suitable computing device. Moreover, an apparatus for performing the invention as well as a product resulting from the invention are within the scope of the teaching and claims.
The present invention is not limited to given embodiments or examples; the attached set of claims in light of the drawings and specification define possible further embodiments for a person skilled in the art.
Claims (3)
Ceff(si,π,w)=F(π,Ceff,S(si,w,Ceff),T(si,w,Ceff)) (3)
Ceff(si,π,w)=F(π,Ceff,S(si,w,Ceff),T(si,w,Ceff)) (3)
Ceff(si,π,w)=F(π,Ceff,S(si,w,Ceff),T(si,w,Ceff)) (3)
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